spynnaker.pyNN.models.neuron.builds package

Module contents

class spynnaker.pyNN.models.neuron.builds.EIFConductanceAlphaPopulation(**kwargs)[source]

Bases: object

Exponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -70.6, 'w': 0.0}
default_parameters = {'a': 4.0, 'b': 0.0805, 'cm': 0.281, 'delta_T': 2.0, 'e_rev_E': 0.0, 'e_rev_I': -80.0, 'i_offset': 0.0, 'tau_m': 9.3667, 'tau_refrac': 0.1, 'tau_syn_E': 5.0, 'tau_syn_I': 0.5, 'tau_w': 144.0, 'v_reset': -70.6, 'v_rest': -70.6, 'v_spike': -40.0, 'v_thresh': -50.4}
class spynnaker.pyNN.models.neuron.builds.HHCondExp(**kwargs)[source]

Bases: object

Single-compartment Hodgkin-Huxley model with exponentially decaying current input.

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}
default_parameters = {'cm': 0.2, 'e_rev_E': 0.0, 'e_rev_I': -80, 'e_rev_K': -90.0, 'e_rev_Na': 50.0, 'e_rev_leak': -65.0, 'g_leak': 0.01, 'gbar_K': 6.0, 'gbar_Na': 20.0, 'i_offset': 0.0, 'tau_syn_E': 0.2, 'tau_syn_I': 2.0, 'v_offset': -63}
class spynnaker.pyNN.models.neuron.builds.IFCondAlpha(**kwargs)[source]

Bases: object

Leaky integrate and fire neuron with an alpha-shaped current input.

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}
default_parameters = {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -70.0, 'i_offset': 0, 'tau_m': 20, 'tau_refrac': 0.1, 'tau_syn_E': 0.3, 'tau_syn_I': 0.5, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -50.0}
class spynnaker.pyNN.models.neuron.builds.IFCondExpBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying conductance input.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCurrAlpha(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an alpha-shaped current-based input.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCurrDualExpBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCurrExpBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFFacetsConductancePopulation(**kwargs)[source]

Bases: object

Leaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1

Warning

Not currently supported by the tool chain.

default_initial_values = {'v': -65.0}
default_parameters = {'e_rev_I': -80, 'g_leak': 40.0, 'tau_syn_E': 30.0, 'tau_syn_I': 30.0, 'v_reset': -80.0, 'v_rest': -65.0, 'v_thresh': -55.0}
class spynnaker.pyNN.models.neuron.builds.IzkCondExpBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Izhikevich neuron model with conductance inputs.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IzkCurrExpBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Izhikevich neuron model with current inputs.

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCondExpStoc(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with a stochastic threshold.

Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical microcircuit models. PLoS Computational Biology. 2013;9(11):e1003311. doi:10.1371/journal.pcbi.1003311

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCurrDelta(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input.

Parameters:
Type:

isyn_inh: float, iterable(float), RandomDistribution or (mapping) function
class spynnaker.pyNN.models.neuron.builds.IFCurrExpCa2Adaptive(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Model from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143

Parameters:
class spynnaker.pyNN.models.neuron.builds.IFCurrExpSEMDBase(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)

Parameters: